Abstract
Stock market predictions help investors to optimize benefits in the financial markets. Various papers have proposed different techniques in stock market forecasting, but no model can provide accurate predictions. In this study, we discuss how to predict stock prices using a MACD (Moving Average Convergence/Divergence Oscillator) method. We collect the dataset, preprocess it, extract features, evaluate the model, and then deploy the MACD method to develop a stock price prediction model. In this study, we collect several features, including date, open, high, low, close, and volume, to conduct the training and testing process. The results of the experiments reveal good accuracy and a low error rate. As a result, it has the potential to be a promising solution for dealing with accurate and dynamic prices. Based on the experimental result, our proposed model can obtain a transaction profit rate of 40.00% and an average profit per transaction of 1.42%.
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